GEOG 566






         Advanced spatial statistics and GIScience

April 5, 2019

A UAS and LiDAR based approach to maximizing forest aesthetics in a timber harvest

Bryan Begay

Research Question: 

Can LiDAR derived from an Unmanned Aerial System (UAS) create a point cloud driven visualization model for maximizing forest aesthetics in a highly visible timber harvest?

Context

A variable retention thinning is planned to be implemented in a harvest unit on the McDonald-Dunn Forest in a visible area near Corvallis. UAS systems offer an efficient way to collect data over large areas to create high quality data sets from LiDAR that can capture the structure of a forest stand. There is a need  for a model/methodology that utilizes UAS LiDAR point clouds to generate a visualization model to create a  timber harvest in an areas with high visibility that maximize forest aesthetics. Inputs for the model include DTMs, Google Earth Pro view shed tool, and point clouds. The point clouds can be manipulated to visualize an optimal silvicultural prescription that maximizes forest landscape aesthetics. Ancillary data of view shed and terrain from DTMs are inputs expected to help create a visualization model.

A description of the data set you will be analyzing, including the spatial and temporal resolution and extent: 

The data set I will be using will include high resolution LiDAR point clouds of a stand, Digital Terrain Models (DTM) from LiDAR point clouds flown by the USFS previously, and additional ancillary data from Google Earth Pro. The Google Earth Pro data will use the view shed tool for assessing the visual impact of regions in the harvesting unit. The spatial resolution will be using high resolution LiDAR point clouds on an area that is a few square kilometers. The temporal resolution will span data acquisition before the harvest, and then an assessment of the computer based prescription after harvest. The temporal resolution of the point cloud collected from the UAV will be collected in a discrete time frame of one day. The DTM data set and google earth pro data sets will be variable, but I anticipate them to be newer high resolution Google Earth imagery and high resolution LiDAR data sets.

Hypotheses:

I hypothesizes that LiDAR point clouds can be used in a visualization model to create a silvicultural prescription in a timber harvest that maximizes forest aesthetics in a logged area . Google Earth Pro view shed tool, high quality LiDAR point clouds, and a large body of literature on forest aesthetics provide a data set that is very rich in inputs to create a visualization model for timber harvests that maximizes forest aesthetics.

Approaches:

I would like to do some sort of analysis looking at the spatial relationship between forest aesthetics and timber harvests. A part of this analysis would look at the relationship of the spatial pattern of residual structure left from the thinning and the landscape aesthetics.

Expected outcome:

I would like an expected outcome to be a visualization model of the harvest unit that utilizes view shed and point clouds that maximizes forest aesthetics in a high viewership area.

Significance:

This spatial problem is important to the profession of forestry as well as other land managers, since it helps maintain the social license for foresters to practice forestry in areas that are highly visible. Public acceptance of harvesting practices is increased when forest aesthetics is taken into account, so creating a methodology and model to assist in creating silvicultural prescriptions that increase forest aesthetics is critical for public acceptance of forestry.

Level of preparation:

A. I have experience in ArcGIS.

B. No experience in modelbuilder and Python programming in GIS.

C. Some experience in R.

D. Experience in Digital Image Processing.

E. I’ve used Google Earth Engine and very little experience with MATLAB.

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1 Comment

  1.   jonesju — April 7, 2019 @ 12:04 pm    

    Bryan, nice beginning. Needs some work: 1) Your research question mixes up the method (LiDAR) with the question (what you want to learn). Try rephrasing as “how does visibility of a timber harvest (A) vary depending on viewer angle and viewer location in the landscape (B), as a result of screening by retained vegetation and landforms (C)?” 2) Hypothesis. Try writing a hypothesis in which you predict which combinations of harvest locations and viewer locations create the least visibility of the harvest. 3) Analyses. Consider what spatial pattern you want to test for in the harvest : clustering? And what properties of the landscape do you want to test: roughness? And what is the spatial pattern of locations of viewers – must be along trails in the Mac-Dunn Forest? 4) Products. Maybe try creating some visualizations of alternative harvest patterns, such as dispersed thinning vs. thinning with skips and gaps (gaps in different locations) and compare their visibility by viewing the simulated LiDAR point clouds from various vantage points along trails?

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